Title:
"Nonparametric Clustering for Data Mining"
Abstract:
The use of density estimation to find clusters in
data is supplementing ad hoc hierarchical
methodology. Examples include finding high-density
regions, finding modes in a kernel density
estimator, and the mode tree. Alternatively,
a mixture model may be fit and the mixture
components associated with individual clusters.
Fitting a high-dimensional mixture model with
many components is difficult to estimate in
practice. Here, we survey mode and level set
methods for finding clusters. We describe a new
algorithm that estimates a subset of a mixture
model. In particular, we demonstrate how to fit one
component at a time and how the fits may be
organized to reveal the complete clustering model.